pred.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json\n",
      "40960/35363 [==================================] - 0s 1us/step\n",
      "49152/35363 [=========================================] - 0s 1us/step\n",
      "[[('n02099601', 'golden_retriever', 0.5354275), ('n02113799', 'standard_poodle', 0.32865775), ('n02104029', 'kuvasz', 0.050152738), ('n02099712', 'Labrador_retriever', 0.017040437), ('n02093647', 'Bedlington_terrier', 0.013122461)]]\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(x_train)\n",
    "pred = vgg16.decode_predictions(prediction)\n",
    "print(pred)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.9.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "dfcd168785218dca7c4bc510ef2c51c49c0c0203e4213351545f8c8481fa3636"
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 },
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